[ { "path": ".gitignore", "content": "# Compiled source #\n###################\n*.com\n*.class\n*.dll\n*.exe\n*.o\n*.so\n\n# Packages #\n############\n# it's better to unpack these files and commit the raw source\n# git has its own built in compression methods\n*.7z\n*.dmg\n*.gz\n*.iso\n*.jar\n*.rar\n*.tar\n*.zip\n\n# Logs and databases #\n######################\n*.log\n*.sql\n*.sqlite\n\n# OS generated files #\n######################\n.DS_Store\n.DS_Store?\n._*\n.Spotlight-V100\n.Trashes\nehthumbs.db\nThumbs.db\n\n# Project Specifics #\n#####################\ndata/test_data/mnist_test.txt\ndata/training_data/mnist_train.txt" }, { "path": "License.md", "content": "MIT License\n\nCopyright (c) 2016 Gil Dekel\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "Matrix.cpp", "content": "//\n// Matrix.cpp\n// Neural Net\n//\n// Created by Gil Dekel on 8/19/16.\n// Last edited by Gil Dekel on 8/30/16.\n//\n\n#include \"Matrix.hpp\"\n\n/*\n * Private members for reference\n *\n * size_t m_size_; // (M)xN\n * size_t n_size_; // Mx(N)\n * double *matrix_; // A pointer to the array.\n * double **rowPtrs_; // An array of row pointers.\n * // used to avoid repeated arithmetics\n * // at each access to the matrix.\n *\n */\n\n\n/**********************************************************\n * Constructors\n **********************************************************/\n\nMatrix::Matrix(size_t m, size_t n) : m_size_{m}, n_size_{n} {\n matrix_ = new double[m_size_ * n_size_]();\n rowPtrs_ = new double*[m_size_];\n \n for (size_t i = 0; i < m_size_; ++i) {\n rowPtrs_[i] = &matrix_[i*n_size_];\n }\n}\n\nMatrix::Matrix(const Matrix &rhs) : m_size_{rhs.m_size_}, n_size_{rhs.n_size_} {\n matrix_ = new double[m_size_ * n_size_]();\n rowPtrs_ = new double*[m_size_];\n \n for (int i = 0; i < m_size_ * n_size_; ++i) {\n matrix_[i] = rhs.matrix_[i];\n }\n \n for (size_t i = 0; i < m_size_; ++i) {\n rowPtrs_[i] = &matrix_[i*n_size_];\n }\n \n}\n\nMatrix& Matrix::operator=(const Matrix &rhs) {\n if (this != &rhs) {\n Matrix copy{rhs};\n std::swap(*this, copy);\n }\n return *this;\n}\n\nMatrix::Matrix(Matrix &&rhs) : m_size_{rhs.m_size_}, n_size_{rhs.n_size_}, matrix_{rhs.matrix_}, rowPtrs_{rhs.rowPtrs_} {\n rhs.m_size_ = 0;\n rhs.n_size_ = 0;\n rhs.matrix_ = nullptr;\n rhs.rowPtrs_ = nullptr;\n}\n\nMatrix& Matrix::operator=(Matrix &&rhs) {\n std::swap(m_size_, rhs.m_size_);\n std::swap(n_size_, rhs.n_size_);\n std::swap(matrix_, rhs.matrix_);\n std::swap(rowPtrs_, rhs.rowPtrs_);\n return *this;\n}\n\nMatrix::~Matrix() {\n delete [] matrix_;\n delete [] rowPtrs_;\n}\n\n/**********************************************************\n * Operator Overloads\n **********************************************************/\n\ndouble& Matrix::operator()(size_t row, size_t col) {\n return rowPtrs_[row][col];\n}\n\nconst double& Matrix::operator()(size_t row, size_t col) const {\n return rowPtrs_[row][col];\n}\n\n\n// ADDITION\nMatrix& Matrix::operator+=(const Matrix & rhs) {\n if (m_size_ == rhs.m_size_ && n_size_ == rhs.n_size_) {\n for (size_t i = 0; i < m_size_ * n_size_; ++i) {\n matrix_[i] += rhs.matrix_[i];\n }\n return *this;\n } else\n throw MatrixDimensionsMismatch();\n}\n\nMatrix& Matrix::operator+=(double scalar) {\n for (size_t i = 0; i < m_size_ * n_size_; ++i) {\n matrix_[i] += scalar;\n }\n return *this;\n}\n\n\n\n// SUBTRACTION\nMatrix& Matrix::operator-=(const Matrix & rhs) {\n if (m_size_ == rhs.m_size_ && n_size_ == rhs.n_size_) {\n for (size_t i = 0; i < m_size_ * n_size_; ++i) {\n matrix_[i] -= rhs.matrix_[i];\n }\n return *this;\n } else\n throw MatrixDimensionsMismatch();\n}\n\nMatrix& Matrix::operator-=(double scalar) {\n for (size_t i = 0; i < m_size_ * n_size_; ++i) {\n matrix_[i] -= scalar;\n }\n return *this;\n}\n\n\n\n// MULTIPLICATION\nMatrix& Matrix::operator*=(const Matrix & rhs) {\n if (m_size_ == rhs.m_size_ && n_size_ == rhs.n_size_) {\n for (size_t i = 0; i < m_size_ * n_size_; ++i) {\n matrix_[i] *= rhs.matrix_[i];\n }\n return *this;\n } else\n throw MatrixDimensionsMismatch();\n}\n\nMatrix& Matrix::operator*=(double scalar) {\n for (size_t i = 0; i < m_size_ * n_size_; ++i) {\n matrix_[i] *= scalar;\n }\n return *this;\n}\n\n\n\n//DIVISION\nMatrix& Matrix::operator/=(const Matrix & rhs) {\n if (m_size_ == rhs.m_size_ && n_size_ == rhs.n_size_) {\n for (size_t i = 0; i < m_size_ * n_size_; ++i) {\n matrix_[i] /= rhs.matrix_[i];\n }\n return *this;\n } else\n throw MatrixDimensionsMismatch();\n}\n\nMatrix& Matrix::operator/=(double scalar) {\n for (size_t i = 0; i < m_size_ * n_size_; ++i) {\n matrix_[i] /= scalar;\n }\n return *this;\n}\n\n\n\n// UNARY NEGATION\nMatrix Matrix::operator-() const {\n Matrix neg{*this};\n for (size_t i = 0; i < m_size_ * n_size_; ++i) {\n neg.matrix_[i] = -neg.matrix_[i];\n }\n return neg;\n}\n\n\n\n\n/**********************************************************\n * Other Functions\n **********************************************************/\n\nMatrix Matrix::dot(const Matrix& rhs) const {\n if (this->n_size_ == rhs.m_size_) {\n Matrix rhs_T{rhs.T()};\n Matrix dproduct(m_size_, rhs.n_size_);\n \n for (size_t i = 0; i < m_size_; ++i) {\n for (size_t j = 0; j < rhs_T.m_size_; ++j) {\n double dot = 0;\n for (size_t k = 0; k < n_size_; ++k) {\n dot += rowPtrs_[i][k] * rhs_T.rowPtrs_[j][k];\n }\n dproduct.rowPtrs_[i][j] = dot;\n }\n }\n return dproduct;\n } else\n throw MatrixInnderDimensionsMismatch();\n}\n\nsize_t Matrix::getNumOfRows() const { return m_size_; }\nsize_t Matrix::getNumOfCols() const { return n_size_; }\n\nMatrix Matrix::T() const {\n Matrix T(n_size_, m_size_);\n for (size_t i = 0; i < m_size_; ++i) {\n for (size_t j = 0; j < n_size_; ++j) {\n T.rowPtrs_[j][i] = rowPtrs_[i][j];\n }\n }\n return T;\n}\n\nstd::pair Matrix::getMaxVal() const {\n long int maxI = -1;\n long int maxJ = -1;\n double maxVal = -INFINITY;\n \n for (size_t i = 0; i < m_size_; ++i) {\n for (size_t j = 0; j < n_size_; ++j) {\n if (rowPtrs_[i][j] >= maxVal) {\n maxVal = rowPtrs_[i][j];\n maxI = i;\n maxJ = j;\n }\n }\n }\n return std::pair(maxI, maxJ);\n}\n\nvoid Matrix::printMtrx() const {\n for (size_t i = 0; i < m_size_; ++i) {\n for (size_t j = 0; j < n_size_; ++j) {\n std::cout << rowPtrs_[i][j] << \"\\t\\t\";\n }\n std::cout << std::endl;\n }\n std::cout << std::endl;\n}\n\n/**********************************************************\n * Non-member, Friend Functions\n **********************************************************/\n\n// ADDITION\nMatrix operator+(Matrix lhs, const Matrix &rhs) {\n if (lhs.m_size_ == rhs.m_size_ && lhs.n_size_ == rhs.n_size_) {\n return lhs += rhs;\n } else\n throw MatrixDimensionsMismatch();\n}\n\nMatrix operator+(Matrix lhs, double scalar) {\n return lhs += scalar;\n}\n\nMatrix operator+(double scalar, Matrix rhs) {\n return rhs += scalar;\n}\n\n\n\n// SUBTRACTION\nMatrix operator-(Matrix lhs, const Matrix &rhs) {\n if (lhs.m_size_ == rhs.m_size_ && lhs.n_size_ == rhs.n_size_) {\n return lhs -= rhs;\n } else\n throw MatrixDimensionsMismatch();\n}\n\nMatrix operator-(Matrix lhs, double scalar) {\n return lhs -= scalar;\n}\n\nMatrix operator-(double scalar, Matrix rhs) {\n return -rhs += scalar;\n}\n\n\n\n// MULTIPLICATION\nMatrix operator*(Matrix lhs, const Matrix &rhs) {\n if (lhs.m_size_ == rhs.m_size_ && lhs.n_size_ == rhs.n_size_) {\n return lhs *= rhs;\n } else\n throw MatrixDimensionsMismatch();\n}\n\nMatrix operator*(Matrix lhs, double scalar) {\n return lhs *= scalar;\n}\n\nMatrix operator*(double scalar, Matrix rhs) {\n return rhs *= scalar;\n}\n\n\n\n//DIVISION\nMatrix operator/(Matrix lhs, const Matrix &rhs) {\n if (lhs.m_size_ == rhs.m_size_ && lhs.n_size_ == rhs.n_size_) {\n return lhs /= rhs;\n } else\n throw MatrixDimensionsMismatch();\n}\n\nMatrix operator/(Matrix lhs, double scalar) {\n return lhs /= scalar;\n}\n\nMatrix operator/(double scalar, Matrix rhs) {\n for (size_t i = 0; i < rhs.m_size_ * rhs.n_size_; ++i) {\n rhs.matrix_[i] = scalar/rhs.matrix_[i];\n }\n return rhs;\n}\n\n\n" }, { "path": "Matrix.hpp", "content": "//\n// Matrix.hpp\n// Neural Net\n//\n// A matrix object, which includes basic operations such as\n// matrix transpose and dot product.\n//\n// Created by Gil Dekel on 8/19/16.\n// Last edited by Gil Dekel on 8/30/16.\n//\n\n#ifndef MATRIX_HPP_\n#define MATRIX_HPP_\n\n#include \n#include // std::swap and std::move\n#include \n#include // INFINITY\n\n#include \"MatrixExceptions.hpp\"\n\nclass Matrix {\npublic:\n \n /**********************************************************\n * Constructors\n **********************************************************/\n \n // Basic ctor to inisialize a matrix of size m by n.\n // All matrix positions will be initialized to 0.\n Matrix(size_t m = 0, size_t n = 0);\n \n // COPY ctor\n Matrix(const Matrix &rhs);\n\n // Copy assignment operator\n Matrix& operator=(const Matrix &rhs);\n\n // MOVE ctor\n Matrix(Matrix &&rhs);\n\n // Move assignment operator\n Matrix& operator=(Matrix &&rhs);\n\n // dealloc matrix_ (dtor)\n ~Matrix();\n \n /**********************************************************\n * Operator Overloads\n **********************************************************/\n \n // A substitute to operator[] for a 2D arrays\n double& operator()(size_t row, size_t col);\n \n const double& operator()(size_t row, size_t col) const;\n \n \n \n // ADDITION\n\n Matrix& operator+=(const Matrix & rhs);\n \n Matrix& operator+=(double scalar);\n \n // Term by term addition operator for two matricies.\n friend Matrix operator+(Matrix lhs, const Matrix &rhs);\n \n // Term by term addition operator for matrix and scalar.\n friend Matrix operator+(Matrix lhs, double scalar);\n \n // Allowing for the scalar addition commutative property.\n friend Matrix operator+(double scalar, Matrix rhs);\n\n\n \n // SUBTRACTION\n \n Matrix& operator-=(const Matrix & rhs);\n \n Matrix& operator-=(double scalar);\n \n // Term by term subtraction operator for two matricies.\n friend Matrix operator-(Matrix lhs, const Matrix &rhs);\n \n // Term by term subtraction operator for matrix and scalar.\n friend Matrix operator-(Matrix lhs, double scalar);\n \n // Term by term subtraction operator for scalar and matrix.\n friend Matrix operator-(double scalar, Matrix rhs);\n \n \n \n // MULTIPLICATION\n \n Matrix& operator*=(const Matrix & rhs);\n \n Matrix& operator*=(double scalar);\n \n // \"Regular\", term by term multiplication operator.\n // See function dot(Matrix &rhs) for dot product.\n friend Matrix operator*(Matrix lhs, const Matrix &rhs);\n \n // \"Regular\" scalar multiplication over matrix.\n friend Matrix operator*(Matrix lhs, double scalar);\n \n // Allowing for the scalar multiplication commutative property.\n friend Matrix operator*(double scalar, Matrix rhs);\n \n \n \n //DIVISION\n \n Matrix& operator/=(const Matrix & rhs);\n \n Matrix& operator/=(double scalar);\n \n // Term by term division operator for two matricies.\n friend Matrix operator/(Matrix lhs, const Matrix &rhs);\n \n // Term by term division operator for matrix and scalar.\n friend Matrix operator/(Matrix lhs, double scalar);\n \n // Term by term division operator for scalar and matrix.\n friend Matrix operator/(double scalar, Matrix rhs);\n \n \n \n // Unary minus operator for Matrix term by term negation\n Matrix operator-() const;\n\n \n \n /**********************************************************\n * Other Functions\n **********************************************************/\n \n // A simple matrix algebra dot product operation.\n // Return a 0 by 0 matrix if the dimensions do not match.\n // See operator*(Matrix &rhs) for term by term multiplication.\n Matrix dot(const Matrix& rhs) const;\n \n // Get number of rows (M)xN\n size_t getNumOfRows() const;\n \n // Get number of columns Mx(N)\n size_t getNumOfCols() const;\n \n // Transpose the matrix MxN -> NxM\n Matrix T() const;\n \n // Get the coordinates of the largest value in the matrix.\n // Will return the coordinates of the earliest larger val.\n std::pair getMaxVal() const;\n \n // Print the matrix to std::cout\n void printMtrx() const;\n \nprivate:\n \n size_t m_size_; // (M)xN\n size_t n_size_; // Mx(N)\n double *matrix_; // A pointer to the array.\n double **rowPtrs_; // An array of row pointers.\n // used to avoid repeated arithmetics\n // at each access to the matrix.\n};\n\n#endif /* MATRIX_HPP_ */\n" }, { "path": "MatrixExceptions.hpp", "content": "//\n// MatrixExceptions.h\n// Neural Net\n//\n// Created by Gil Dekel on 8/28/16.\n// Last edited by Gil Dekel on 8/28/16.\n//\n\n#ifndef MATRIX_EXCEPTIONS_HPP\n#define MATRIX_EXCEPTIONS_HPP\n\nclass MatrixDimensionsMismatch : public std::exception {\n const char* what() const noexcept {return \"Matrix dimensions must agree.\\n\";}\n};\n\nclass MatrixInnderDimensionsMismatch : public std::exception {\n const char* what() const noexcept {return \"Matrix inner dimensions must agree.\\n\";}\n};\n\n\n#endif /* MATRIX_EXCEPTIONS_HPP */\n" }, { "path": "NeuralNet.cpp", "content": "//\n// NeuralNet.cpp\n// Neural Net\n//\n// Created by Gil Dekel on 8/19/16.\n// Last edited by Gil Dekel on 8/30/16.\n//\n\n#include \n\n#include \"NeuralNet.hpp\"\n\n/*\n * Private members for reference\n *\n * size_t inNodes_;\n * size_t hiddNodes_;\n * size_t outNodes_;\n * size_t hiddLayers_;\n * double LR_;\n *\n * std::vector weights_;\n * std::vector outputs_;\n *\n */\n\n/**********************************************************\n * Constructors\n **********************************************************/\n\nNeuralNet::NeuralNet(size_t inputNodes, size_t hiddenNodes, size_t outputNodes, size_t hiddenLayers, double learningRate )\n : inNodes_{inputNodes}, hiddNodes_{hiddenNodes}, outNodes_{outputNodes}, hiddLayers_{hiddenLayers}, LR_{learningRate},\n weights_{std::vector()}, outputs_{std::vector()} {\n \n size_t weightsSize = 1+hiddLayers_;\n weights_.reserve(weightsSize);\n \n size_t outputsSize = 2+hiddLayers_;\n outputs_.reserve(outputsSize);\n \n for (size_t i = 0; i < weightsSize; ++i) {\n size_t currLayer = 0;\n size_t nextLayer = 0;\n \n if (i == 0) {\n currLayer = inNodes_;\n nextLayer = hiddNodes_;\n } else if (i == weightsSize-1) {\n currLayer = hiddNodes_;\n nextLayer = outNodes_;\n } else {\n currLayer = hiddNodes_;\n nextLayer = hiddNodes_;\n }\n \n weights_.push_back(initializeMatrix(nextLayer, currLayer));\n }\n \n for (size_t i = 0; i < outputsSize; ++i) {\n size_t numOfNodes = 0;\n \n if (i == 0) {\n numOfNodes = inNodes_;\n } else if (i == outputsSize-1) {\n numOfNodes = outNodes_;\n } else {\n numOfNodes = hiddNodes_;\n }\n \n outputs_.push_back(Matrix(numOfNodes, 1));\n }\n}\n\nNeuralNet::NeuralNet(const std::string &filename) {\n if (filename.substr(filename.length()-3).compare(\".nn\") != 0) {\n std::cout << \"ERROR:: FILE MUST BE OF TYPE *.nn\\n\";\n exit(1);\n }\n \n std::ifstream in(filename);\n if (in.fail()) {\n std::cout << \"ERROR:: CANNOT READ FROM FILE: '\" << filename << \"'\\n\";\n exit(1);\n }\n \n in >> inNodes_ >> hiddNodes_ >> outNodes_ >> hiddLayers_ >> LR_;\n \n weights_ = std::vector();\n size_t weightsSize = 1+hiddLayers_;\n weights_.reserve(weightsSize);\n \n outputs_ = std::vector();\n size_t outputsSize = 2+hiddLayers_;\n outputs_.reserve(outputsSize);\n \n size_t Mrows = 0, Ncols = 0;\n double nextVal = 0;\n \n for (size_t i = 0; i < weightsSize; ++i) {\n in >> Mrows >> Ncols;\n weights_.push_back(Matrix(Mrows, Ncols));\n \n for (size_t m = 0; m < Mrows; ++m) {\n for (size_t n = 0; n < Ncols; ++n) {\n in >> nextVal;\n weights_.back()(m,n) = nextVal;\n }\n }\n }\n \n for (size_t i = 0; i < outputsSize; ++i) {\n size_t numOfNodes = 0;\n \n if (i == 0) {\n numOfNodes = inNodes_;\n } else if (i == outputsSize-1) {\n numOfNodes = outNodes_;\n } else {\n numOfNodes = hiddNodes_;\n }\n \n outputs_.push_back(Matrix(numOfNodes, 1));\n }\n}\n\n/**********************************************************\n * Other Functions\n **********************************************************/\n\nMatrix NeuralNet::queryNet(const Matrix &inputList) {\n Matrix finalOutput{inputList.T()};\n outputs_[0] = finalOutput;\n \n for (size_t i = 0; i < weights_.size(); ++i) {\n finalOutput = weights_[i].dot(finalOutput);\n \n for (size_t m = 0; m < finalOutput.getNumOfRows(); ++ m) {\n for (size_t n = 0; n < finalOutput.getNumOfCols(); ++n) {\n finalOutput(m,n) = activationFunction(finalOutput(m,n));\n }\n }\n outputs_[i+1] = finalOutput;\n }\n \n return finalOutput;\n}\n\nvoid NeuralNet::trainingCycle(const Matrix &inputList, const Matrix &targetOutput) {\n Matrix currOutput{queryNet(inputList)}; // Returned transposed\n Matrix currTargetOut{targetOutput.T()};\n Matrix currLayerErrors{currTargetOut-currOutput}; // Calculate the final output layer's error\n \n // Update the weights going from the output nodes back\n for (long int i = weights_.size()-1; i >= 0; --i) {\n Matrix prevLayerErrors{weights_[i].T().dot(currLayerErrors)};\n Matrix prevHiddLayerOutsT{outputs_[i].T()};\n \n Matrix deltaWeights{currLayerErrors*currOutput};\n deltaWeights *= (1-currOutput);\n deltaWeights = deltaWeights.dot(prevHiddLayerOutsT);\n deltaWeights *= LR_;\n weights_[i] += deltaWeights;\n \n currLayerErrors = prevLayerErrors;\n currOutput = outputs_[i];\n }\n}\n\nvoid NeuralNet::saveNetwork(const std::string &name) const {\n std::string fileName;\n \n if (name.empty())\n fileName = (getCurrTime() + \".nn\");\n else\n fileName = (name + \".nn\");\n \n std::ofstream out(fileName);\n if (out.fail()) {\n std::cout << \"ERROR:: Fails writing to file \" << (getCurrTime() + \".nn\") << std::endl;\n exit(1);\n }\n \n out << inNodes_ << \" \" << hiddNodes_ << \" \" << outNodes_ << \" \" << hiddLayers_ << \" \" << LR_ << std::endl;\n \n for (size_t i = 0; i < weights_.size(); ++i) {\n out << weights_[i].getNumOfRows() << \" \" << weights_[i].getNumOfCols() << std::endl;\n for (size_t m = 0; m < weights_[i].getNumOfRows(); ++m) {\n for (size_t n = 0; n < weights_[i].getNumOfCols(); ++n) {\n out << weights_[i](m,n) << \" \";\n }\n out << std::endl;\n }\n }\n}\n\nvoid NeuralNet::loadNetwork(const std::string &name) {\n *this = NeuralNet(name);\n}\n\n/**********************************************************\n * Private Functions\n **********************************************************/\n\nMatrix NeuralNet::initializeMatrix(size_t rows, size_t cols) const {\n Matrix init(rows,cols);\n std::default_random_engine generator((std::random_device()()));\n std::normal_distribution distribution(0.0, std::pow(rows, -0.5));\n \n for (size_t m = 0; m < rows; ++m) {\n for (size_t n = 0; n < cols; ++n) {\n init(m,n) = distribution(generator);\n }\n }\n return init;\n}\n\n// The activation function. Currently using Sigmoid function.\ndouble NeuralNet::activationFunction(double x) const {\n return 1/(1+std::exp(-x));\n}\n\nstd::string NeuralNet::getCurrTime() const {\n time_t t = time(0); // get time now\n struct tm * now = localtime( & t );\n std::string currTime = std::to_string(now->tm_year + 1900) + '-' + std::to_string(now->tm_mon + 1) + '-' + std::to_string(now->tm_mday);\n currTime += \"--\" + ((now->tm_hour < 10) ? \"0\" + std::to_string(now->tm_hour) : std::to_string(now->tm_hour));\n currTime += \"-\" + ((now->tm_min < 10) ? \"0\" + std::to_string(now->tm_min) : std::to_string(now->tm_min));\n currTime += \"-\" + ((now->tm_sec < 10) ? \"0\" + std::to_string(now->tm_sec) : std::to_string(now->tm_sec));\n \n return currTime;\n}\n" }, { "path": "NeuralNet.hpp", "content": "//\n// NeuralNet.hpp\n// Neural Net\n//\n// Created by Gil Dekel on 8/19/16.\n// Last edited by Gil Dekel on 8/30/16.\n//\n\n#ifndef NEURALNET_HPP_\n#define NEURALNET_HPP_\n\n#include \n#include \n#include // std::swap and std::move\n#include \n#include \n#include // for std::pow()\n#include \n#include \"Matrix.hpp\"\n\n\nclass NeuralNet {\npublic:\n\n /**********************************************************\n * Constructors\n **********************************************************/\n \n // Default ctor to initialize a new neural net object.\n NeuralNet(size_t inputNodes = 1, size_t hiddenNodes = 1,\n size_t outputNodes = 1, size_t hiddenLayers = 1, double learningRate = 0.1);\n \n // A ctor that loads a saved neural net.\n // Expecting a filename with file type *.nn\n // Initializes a new net wit the weights saved\n // in the file.\n explicit NeuralNet(const std::string &filename);\n \n /**********************************************************\n * Other Functions\n **********************************************************/\n \n // Feed forward the input Matrix and return the\n // net's prediction in a Matrix.\n Matrix queryNet(const Matrix &inputList);\n \n // A single feed forward and back propagation with weight updates.\n void trainingCycle(const Matrix &inputList, const Matrix &targetOutput);\n \n // A method to save the current state of the net.\n // Files are saved as *.nn.\n // If no file name is supplied, file will be saved\n // with the time and date the net was saved.\n void saveNetwork(const std::string &name = \"\") const;\n \n // Load an existing net into the current object.\n void loadNetwork(const std::string &name);\n \nprivate:\n // Initializes the weights (parameters) between the different layers\n // Initial values are being drawn from a normal distribution centered\n // at 0, with standard deviation of (number_of_inputs_to_nodes_in_next_layer)^(-0.5)\n Matrix initializeMatrix(size_t m, size_t n) const;\n \n // The activation function. Currently using Sigmoid function.\n double activationFunction(double x) const;\n \n // A utility function to get the current time in a string.\n // Used to name neural nets when saved.\n std::string getCurrTime() const;\n \n \n size_t inNodes_; // Number of input nodes\n size_t hiddNodes_; // Number of nodes per hidden layer\n size_t outNodes_; // Number of output nodes\n size_t hiddLayers_; // Number of hidden layers\n double LR_; // The learning rate.\n \n std::vector weights_; // The weight matrices\n std::vector outputs_; // Intermediate outputs of all the layers are requierd for training.\n // this is where they are stored.\n};\n\n\n\n#endif /* NEURALNET_HPP_ */\n" }, { "path": "README.md", "content": "\n \"N++\"\n\n\n\n# NN++\n\nA short, self-contained, and easy-to-use neural net implementation for C++. It includes the neural net implementation and a Matrix class for *basic* linear algebra operations. This project is mostly for **learning purposes**, but preliminary testing results over the MNIST dataset show some promise.\n\n## Getting Started\n\nThese instructions will get you a copy of the net up and running on your local machine for development and testing purposes.\n\n### Prerequisites\n\nAny compiler that can handle C++11.\n\n### Installing\n\n1. Download `Matrix.hpp`, `Matrix.cpp`, `NeuralNet.hpp`, and `NeuralNet.cpp` and place them in your project's working directory.\n2. Include the headers in your main driver program (e.g. `main.cpp`).\n\n```\n#include \"Matrix.hpp\"\n#include \"NeuralNet.hpp\"\n```\n_**NOTE:** It is not required to `#include \"Matrix.hpp\"` since it is included within `NeuralNet.hpp`. However it is probably better to do so for clarity and safety in case you plan on using Matrix objects in your code (and you probably will if you use NeuralNet)._\n## Example Code\n### The Matrix Class\nFirst you need to know how to use the Matrix class.\nMatrix objects are basically 2D-vectors with built-in linear algebra operations.\n\n#### Matrix Initialization\n\n```\nMatrix A; // Initializes a 0x0 matrix.\nMatrix B(2,2) // Initializes a 2x2 matrix with all zeros. Values are doubles.\nMatrix C(2,1) // Initializes a 2x1 matrix.\n```\n#### Element Access\nTo access/modify a value in a matrix, use `operator()`, NOT `operator[]`:\n\n```\nB(0,0) = 1; B(0,1) = 2; B(1,0) = 3; B(1,1) = 4; // [1 2]\n // [3 4]\n\nC(0,0) = 1; C(1,0) = 2; // [1]\n // [2]\n```\n\n#### Matrix Term-by-Term Addition/Subtraction/Multiplication\n```\n// Commutative property is supported for addition\nMatrix D = B+B; // D = [2 4]\n [6 8]\n \nMatrix E = B-B; // E = [0 0]\n [0 0]\n \n// Commutative property is supported for multiplication \nMatrix F = B*B // F = [1 4]\n [9 16]\n \n// Mismatching matrix dimensions in term-by-term operations\n// is illegal and a MatrixDimensionsMismatch exception will be thrown.\nMatrix G = B+C; // Throws MatrixDimensionsMismatch()\nMatrix G = B-C; // Throws MatrixDimensionsMismatch()\nMatrix G = B*C; // Throws MatrixDimensionsMismatch()\n```\n\n#### Matrix and Scalars\n```\n// Commutative property is supported for addition\nMatrix BplusTwo = B+2; // (== 2+B) BplusTwo = [3 4]\n [5 6]\n\nMatrix CminusTwo = C-2; // CminusTwo = [-1]\n [ 0]\n \nMatrix TwominusB = 2-C; // TwominusB = [ 1]\n [ 0]\n \n// Commutative property is supported for multiplication\nMatrix BtimesThree = B*3; // (== 3*B) BtimesThree = [3 6]\n [9 12]\n```\n\n#### Matrix Multiplication (Dot Product)\n```\nMatrix BB = B.dot(B); // BB = [ 7 10]\n [15 22]\n \nMatrix BC = B.dot(C); // BC = [ 5]\n [11]\n\n// Mismatching the number of columns in the left-hand-side matrix\n// with the number of rows in the right-hand-side matrix is illegal\n// A MatrixInnderDimensionsMismatch exception will be thrown.\nMatrix CB = C.dot(B); // Throws MatrixInnderDimensionsMismatch()\n```\n\n#### Matrix Transpose\n```\nMatrix B_T = B.T(); // B_T = [1 3]\n [2 4]\n \nMatrix C_T = C.T(); // C_T = [1 2]\n```\n\n#### An Example of Populating a 4x3 Matrix\n```\nint m = 4;\nint n = 3;\n\nMatrix mtrx(m,n);\n\nint count = 1;\nfor (int i = 0; i < mtrx.getNumOfRows(); ++i) {\n for (int j = 0; j < mtrx.getNumOfCols(); ++j) {\n mtrx(i,j) = count;\n ++count;\n }\n}\n```\nThis will result with `mtrx` ==\n```\n[ 1 2 3]\n[ 4 5 6]\n[ 7 8 9]\n[10 11 12]\n```\n\n### The NeuralNet Class\n#### Neural Net Initialization (The Parameters)\nWhen initialized, a net takes in five parameters: \n1. Number of input nodes. \n2. Number of nodes per hidden layer. \n3. Number of output nodes. \n4. Number of hidden layers. \n5. The learning rate. \n\n```\nNeuralNet NN(4, 3, 1, 10, 0.1);\n```\n_This_ particular neural net has 4 input nodes, 1 hidden layer with 3 nodes, 10 output node, and has a learning rate of 0.1. \nNew neural nets' weights are initialized with values drawn from a normal distribution centered at 0, with standard deviation that is equal to `1/sqrt(number_of_inputs_to_nodes_in_next_layer)`. In other words, small negative and positive values that are proportional to the size of their previous layer.\n\n#### A Training Cycle\nOnce the net is initialized, it is ready to do work. \n__ONE__ training cycle == one feed forward and one back propagation with weight adjustments. \n \nTo train one cycle, the input data must be parsed into a Matrix object with dimensions: `1xnumber_of_input_nodes` (1x4 in our case), and the target output must be parsed into a Matrix object with dimensions: `1xnumber_of_output_nodes` (1x10 in our case). \n```\nMatrix input(1,4);\ninput(0,0) = 0.3;\ninput(0,1) = -0.1;\ninput(0,2) = 0.2;\ninput(0,3) = 0.8;\n\nMatrix targetOutput(1,1);\ntarget(0,0) = 0.5;\ntarget(0,1) = -0.3;\n .\n .\n .\ntarget(0,9) = 0.23; // Obviously, matrices should be populated using\n // some parser and not manualy like this.\n\n```\nThen, simply execute the training cycle on the data as follows:\n```\nNN.trainingCycle(input, targetOutput);\n```\nRepeate the process over all training instances.\n\n#### Querying the Net\nOnce the training phase is complete, you can query it as follows: \n(Technically speaking, you can query it right after initialization).\n\nParse the query into a Matrix like parsed the training instance:\n```\nMatrix query(1,2);\ninput(0,0) = 0.5;\ninput(0,1) = -0.2;\ninput(0,2) = -0.3;\ninput(0,3) = 0.4;\n```\n\nQuery the net and catch the result:\n```\nMatrix prediction = NN.queryNet(query); // Will return a 1x10 Matrix object with net's prediction\n```\nAND THAT'S IT!\n\n## TODO\n1. Add `array`, `std::vector`, and `std::initializer_list` constructors to the Matrix class\n2. Either improve on or replace my Matrix class for better/faster performance\n3. Add multiple epoch learning with early stopping.\n\n## Authors\n\n* **Gil Dekel** - *Initial implementation* - [stagadish](https://github.com/stagadish)\n\nSee also the list of [contributors](https://github.com/stagadish/NNplusplus/contributors) who participated in this project.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](https://github.com/stagadish/NNplusplus/blob/master/License.md) file for details\n" }, { "path": "data/test_data/mnist_test_10.txt", "content": "7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 84 185 159 151 60 36 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 222 254 254 254 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 69 220 231 123 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18 205 253 176 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17 125 253 185 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 71 214 231 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 167 253 225 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 72 205 207 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 30 249 233 49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 32 253 89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0" }, { "path": "main.cpp", "content": "//\n// main.cpp\n// Neural Net\n//\n// Created by Gil Dekel on 8/21/16.\n// Last edited by Gil Dekel on 9/4/16.\n//\n\n#include \n#include \n#include //std::rand, std::srand\n#include //std::accumulate\n#include \n#include \n#include \n\n\n#include \"NeuralNet.hpp\"\n#include \"Matrix.hpp\"\n\nvoid parseInput(const std::string &fileName, std::vector &inputs, std::vector &targetOutputs);\nstd::string getCurrTime();\nint myrandom(int i) { return std::rand()%i;}\n\nint main(int argc, const char * argv[]) {\n std::srand(unsigned(std::time(0)));\n \n //***********\n // Parameters\n //***********\n \n int CV_k = 10; // k == 10 in k-fold cross validation\n \n int nodes_in = 784; // input is 28x28 pics of hand written digits parsed into 1x784 horizontal vectors\n int nodes_out = 10; // output is a vector of size 10, each location represents a different number between 0-9\n int nodes_hidd = (nodes_in+nodes_out)/2; // number of hidden nodes per hidden lyer is the average between in and out nodes\n int num_hidd_layers = 1; // number of hidden layers\n double LR = 0.1; // the learning rate of the net\n \n //*********************************************************\n // Prepare data for training using 10-fold cross validation\n //*********************************************************\n \n std::cout << \"Parsing TRAINING data...\\n\";\n \n std::vector trainingInputs;\n std::vector trainingTargetOutputs;\n parseInput(\"data/training_data/mnist_train_100.txt\", trainingInputs, trainingTargetOutputs);\n std::cout << \"Number of instances: \" << trainingInputs.size() << std::endl;\n std::cout << \"Size of inputs: \" << trainingInputs.size() << \" and size of targetOutputs: \" << trainingTargetOutputs.size() << std::endl;\n std::cout << \"Size of inputs matrices: \" << trainingInputs[78].getNumOfRows() << \",\" << trainingInputs[78].getNumOfCols() << \" and size of targetOutputs matrices: \" << trainingTargetOutputs[78].getNumOfRows() << \",\" << trainingTargetOutputs[78].getNumOfCols() << std::endl;\n\n \n // Get a vector of shuffled indices. Effectively shuffling the data.\n std::vector shuffledIdxs(trainingInputs.size());\n for (int i = 0; i < shuffledIdxs.size(); ++i) {\n shuffledIdxs[i] = i;\n }\n std::random_shuffle(shuffledIdxs.begin(), shuffledIdxs.end(), myrandom);\n\n size_t cvClusters = shuffledIdxs.size()/CV_k;\n \n //****************************************\n // Begin 10-fold cross validation training\n //****************************************\n \n std::vector NNs(CV_k);\n std::vector accuracies(CV_k);\n size_t total_num_of_tests = 0;\n \n std::cout << \"\\n\\nTRAINING BEGINS!\\n\\twith \" << CV_k << \"-fold cross validation.\\n\\tSTART time: \" << getCurrTime() << std::endl << std::endl;\n for (int i = 0; i < CV_k; ++i) {\n \n size_t numOfTrainingInstances = 0;\n \n std::cout << \"************ Training k = \" << i+1 << \" ************\" << \"\\nSTART time: \" << getCurrTime() << std::endl;\n NNs[i] = new NeuralNet(nodes_in, nodes_hidd, nodes_out, num_hidd_layers, LR);\n \n for (int j = 0; j < shuffledIdxs.size(); ++j) {\n if (j < cvClusters*i || j > cvClusters*i + cvClusters-1) {\n ++numOfTrainingInstances;\n NNs[i]->trainingCycle(trainingInputs[shuffledIdxs[j]], trainingTargetOutputs[shuffledIdxs[j]]);\n }\n }\n\n size_t numOfTests = 0;\n double success = 0;\n double fails = 0;\n\n std::cout << \"Testing k = \" << i+1 << std::endl;\n for (int j = 0; j < shuffledIdxs.size(); ++j) {\n if (j >= cvClusters*i && j <= cvClusters*i + cvClusters-1) {\n ++numOfTests;\n Matrix result = NNs[i]->queryNet(trainingInputs[shuffledIdxs[j]]);\n \n std::pair resultVal = result.getMaxVal();\n std::pair targetVal = trainingTargetOutputs[shuffledIdxs[j]].T().getMaxVal();\n \n if (resultVal == targetVal) {\n ++success;\n } else {\n ++fails;\n }\n }\n }\n \n accuracies[i] = success/numOfTests;\n total_num_of_tests += numOfTests;\n std::cout << \"\\tNumber of training instances: \" << numOfTrainingInstances << \"\\n\\tNumber of tests: \" << numOfTests << \"\\n\\tAccuracy: \" << accuracies[i]*100 << \"%\";\n std::cout << \"\\nEND time: \" << getCurrTime() << std::endl << std::endl;\n }\n \n //********************************\n // Compute the confidence interval\n //********************************\n \n // Find the Mean and standard deviation of the accuracies\n double mean_accu = 0;\n for (int k = 0; k < accuracies.size(); ++k) {\n mean_accu += accuracies[k];\n }\n mean_accu = mean_accu/accuracies.size();\n \n double standard_deviation = 0;\n for (int i = 0; i < accuracies.size(); ++i) {\n standard_deviation += pow((accuracies[i]-mean_accu),2);\n }\n standard_deviation = sqrt(standard_deviation/accuracies.size());\n \n \n // Find the margin of error for a 95% confidence interval assuming n => 30\n double standard_error = standard_deviation/(sqrt(accuracies.size()));\n double margin_of_error = standard_error*1.96;\n \n std::pair confidence_interval(mean_accu-margin_of_error, mean_accu+margin_of_error);\n \n // Find the best performing Net\n double maxAccuVal = 0;\n int idxOfBestNN = -1;\n for (int k = 0; k < accuracies.size(); ++k) {\n if (maxAccuVal < accuracies[k]) {\n maxAccuVal = accuracies[k];\n idxOfBestNN = k;\n }\n }\n \n //****************************\n // Print final training report\n //****************************\n \n std::cout << \"TRAINING ENDED!\\n\\twith \" << CV_k << \"-fold cross validation.\\n\\tEND time: \" << getCurrTime() << std::endl << std::endl;\n std::cout << \"\\tTotal num of tests: \" << total_num_of_tests << \"\\n\\tMean accuracy: \" << mean_accu*100 << \"%\";\n std::cout << \"\\n\\t95% Confidence Interval: \" << confidence_interval.first*100 << \"% to \" << confidence_interval.second*100\n << \"%\\t[\" << mean_accu*100 << \"% ± \" << margin_of_error*100 << \"%]\\n\";\n std::cout << \"\\t***********************************************************\\n\";\n std::cout << \"\\t* Best performing NN is net No. \" << idxOfBestNN+1 << \" with accuracy of: \" << accuracies[idxOfBestNN]*100 << \"%\\n\";\n std::cout << \"\\t***********************************************************\\n\\n\\n\\n\";\n \n \n \n\n //********************************************\n // Prepare data for testing using the best net\n //********************************************\n \n std::cout << \"Parsing TESTING data...\\n\";\n \n std::vector testInputs;\n std::vector testTargetOutputs;\n parseInput(\"data/test_data/mnist_test_10.txt\", testInputs, testTargetOutputs);\n std::cout << \"Number of instances: \" << testInputs.size() << std::endl;\n std::cout << \"Size of inputs: \" << testInputs.size() << \" and size of targetOutputs: \" << testTargetOutputs.size() << std::endl;\n std::cout << \"Size of inputs matrices: \" << testInputs[0].getNumOfRows() << \",\" << testInputs[0].getNumOfCols() << \" and size of targetOutputs matrices: \" << testTargetOutputs[0].getNumOfRows() << \",\" << testTargetOutputs[0].getNumOfCols() << std::endl;\n\n \n double numOfTests = testInputs.size();\n double success = 0;\n double fails = 0;\n \n std::cout << \"\\n\\nTESTING BEGINS!\" << std::endl;\n for (int i = 0; i < testInputs.size(); ++i) {\n Matrix result = NNs[idxOfBestNN]->queryNet(testInputs[i]);\n \n std::pair resultVal = result.getMaxVal();\n std::pair targetVal = testTargetOutputs[i].T().getMaxVal();\n\n if (resultVal == targetVal) {\n ++success;\n } else {\n ++fails;\n }\n\n }\n \n std::cout << \"\\tNumber of successful classifications: \" << success << \"\\n\\tNumber of failed classifications: \" <<\n fails << \"\\n\\tAccuracy: \" << success/numOfTests << std::endl;\n std::cout << \"TESTING ENDED!\\n\";\n \n// NNs[idxOfBestNN]->saveNetwork();\n \n return 0;\n}\n\nvoid parseInput(const std::string &fileName, std::vector &inputs, std::vector &targetOutputs) {\n std::ifstream in(fileName);\n if (in.fail()) {\n std::cout << \"ERROR:: CANNOT READ FROM FILE: '\" << fileName << \"'\\n\";\n exit(1);\n }\n \n std::string instance;\n while (getline(in, instance)) {\n std::stringstream ss(instance);\n size_t target;\n double pixel;\n\n ss >> target;\n Matrix newTarget(1,10);\n for (int m = 0; m < newTarget.getNumOfRows(); ++m) {\n for (int n = 0; n < newTarget.getNumOfCols(); ++n) {\n newTarget(m,n) = 0.01;\n }\n }\n newTarget(0,target) = 0.99;\n\n targetOutputs.push_back(std::move(newTarget));\n\n Matrix instanceInput(1,784);\n size_t count = 0;\n while (ss >> pixel) {\n instanceInput(0,count) = ((pixel/255)*0.99)+0.01;\n ++count;\n }\n inputs.push_back(std::move(instanceInput));\n }\n \n}\n\nstd::string getCurrTime() {\n time_t t = time(0); // get time now\n struct tm * now = localtime( & t );\n std::string currTime = ((now->tm_hour < 10) ? \"0\" + std::to_string(now->tm_hour) : std::to_string(now->tm_hour));\n currTime += \":\" + ((now->tm_min < 10) ? \"0\" + std::to_string(now->tm_min) : std::to_string(now->tm_min));\n currTime += \":\" + ((now->tm_sec < 10) ? \"0\" + std::to_string(now->tm_sec) : std::to_string(now->tm_sec));\n currTime += ' ' + std::to_string(now->tm_mon + 1) + '/' + std::to_string(now->tm_mday) + '/' + std::to_string(now->tm_year + 1900);\n \n return currTime;\n}" }, { "path": "reports/60000 training dataset report.txt", "content": "Parsing TRAINING data...\nNumber of instances: 60000\nSize of inputs: 60000 and size of targetOutputs: 60000\nSize of inputs matrices: 1,784 and size of targetOutputs matrices: 1,10\n\n\nTRAINING BEGINS!\n\twith 10-fold cross validation.\n\tSTART time: 00:12:45 8/24/2016\n\n************ Training k = 1 ************\nSTART time: 00:12:45 8/24/2016\nTesting k = 1\n\tNumber of training instances: 54000\n\tNumber of tests: 6000\n\tAccuracy: 96.1%\nEND time: 00:54:46 8/24/2016\n\n************ Training k = 2 ************\nSTART time: 00:54:46 8/24/2016\nTesting k = 2\n\tNumber of training instances: 54000\n\tNumber of tests: 6000\n\tAccuracy: 95.8333%\nEND time: 01:36:16 8/24/2016\n\n************ Training k = 3 ************\nSTART time: 01:36:16 8/24/2016\nTesting k = 3\n\tNumber of training instances: 54000\n\tNumber of tests: 6000\n\tAccuracy: 96.1667%\nEND time: 02:18:51 8/24/2016\n\n************ Training k = 4 ************\nSTART time: 02:18:51 8/24/2016\nTesting k = 4\n\tNumber of training instances: 54000\n\tNumber of tests: 6000\n\tAccuracy: 95.7667%\nEND time: 03:00:35 8/24/2016\n\n************ Training k = 5 ************\nSTART time: 03:00:35 8/24/2016\nTesting k = 5\n\tNumber of training instances: 54000\n\tNumber of tests: 6000\n\tAccuracy: 95.8167%\nEND time: 03:42:15 8/24/2016\n\n************ Training k = 6 ************\nSTART time: 03:42:15 8/24/2016\nTesting k = 6\n\tNumber of training instances: 54000\n\tNumber of tests: 6000\n\tAccuracy: 96.3167%\nEND time: 04:24:07 8/24/2016\n\n************ Training k = 7 ************\nSTART time: 04:24:07 8/24/2016\nTesting k = 7\n\tNumber of training instances: 54000\n\tNumber of tests: 6000\n\tAccuracy: 95.9833%\nEND time: 05:05:49 8/24/2016\n\n************ Training k = 8 ************\nSTART time: 05:05:49 8/24/2016\nTesting k = 8\n\tNumber of training instances: 54000\n\tNumber of tests: 6000\n\tAccuracy: 96.1833%\nEND time: 05:47:28 8/24/2016\n\n************ Training k = 9 ************\nSTART time: 05:47:28 8/24/2016\nTesting k = 9\n\tNumber of training instances: 54000\n\tNumber of tests: 6000\n\tAccuracy: 95.8833%\nEND time: 06:29:07 8/24/2016\n\n************ Training k = 10 ************\nSTART time: 06:29:07 8/24/2016\nTesting k = 10\n\tNumber of training instances: 54000\n\tNumber of tests: 6000\n\tAccuracy: 96.4833%\nEND time: 07:11:00 8/24/2016\n\nTRAINING ENDED!\n\twith 10-fold cross validation.\n\tEND time: 07:11:00 8/24/2016\n\n\tTotal num of tests: 60000\n\tMean accuracy: 96.0533%\n\t95% Confidence Interval: 95.9138% to 96.1928%\t[96.0533% ± 0.139499%]\n\n\t\n\n\t======================| Please ignore everything beyond this point |======================\n\n\t***********************************************************\n\t* Best performing NN is net No. 10 with accuracy of: 96.4833%\n\t***********************************************************\n\n\n\nParsing TESTING data...\nNumber of instances: 10000\nSize of inputs: 10000 and size of targetOutputs: 10000\nSize of inputs matrices: 1,784 and size of targetOutputs matrices: 1,10\n\n\nTESTING BEGINS!\n\tNumber of successful classifications: 9659\n\tNumber of failed classifications: 341\n\tAccuracy: 0.9659\nTESTING ENDED!" }, { "path": "reports/example report.txt", "content": "Parsing TRAINING data...\nNumber of instances: 100\nSize of inputs: 100 and size of targetOutputs: 100\nSize of inputs matrices: 1,784 and size of targetOutputs matrices: 1,10\n\n\nTRAINING BEGINS!\n\twith 10-fold cross validation.\n\tSTART time: 16:11:24 8/23/2016\n\n************ Training k = 1 ************\nSTART time: 16:11:24 8/23/2016\nTesting k = 1\n\tNumber of training instances: 90\n\tNumber of tests: 10\n\tAccuracy: 70%\nEND time: 16:11:28 8/23/2016\n\n************ Training k = 2 ************\nSTART time: 16:11:28 8/23/2016\nTesting k = 2\n\tNumber of training instances: 90\n\tNumber of tests: 10\n\tAccuracy: 80%\nEND time: 16:11:33 8/23/2016\n\n************ Training k = 3 ************\nSTART time: 16:11:33 8/23/2016\nTesting k = 3\n\tNumber of training instances: 90\n\tNumber of tests: 10\n\tAccuracy: 80%\nEND time: 16:11:37 8/23/2016\n\n************ Training k = 4 ************\nSTART time: 16:11:37 8/23/2016\nTesting k = 4\n\tNumber of training instances: 90\n\tNumber of tests: 10\n\tAccuracy: 70%\nEND time: 16:11:42 8/23/2016\n\n************ Training k = 5 ************\nSTART time: 16:11:42 8/23/2016\nTesting k = 5\n\tNumber of training instances: 90\n\tNumber of tests: 10\n\tAccuracy: 70%\nEND time: 16:11:46 8/23/2016\n\n************ Training k = 6 ************\nSTART time: 16:11:46 8/23/2016\nTesting k = 6\n\tNumber of training instances: 90\n\tNumber of tests: 10\n\tAccuracy: 50%\nEND time: 16:11:50 8/23/2016\n\n************ Training k = 7 ************\nSTART time: 16:11:50 8/23/2016\nTesting k = 7\n\tNumber of training instances: 90\n\tNumber of tests: 10\n\tAccuracy: 80%\nEND time: 16:11:55 8/23/2016\n\n************ Training k = 8 ************\nSTART time: 16:11:55 8/23/2016\nTesting k = 8\n\tNumber of training instances: 90\n\tNumber of tests: 10\n\tAccuracy: 60%\nEND time: 16:11:59 8/23/2016\n\n************ Training k = 9 ************\nSTART time: 16:11:59 8/23/2016\nTesting k = 9\n\tNumber of training instances: 90\n\tNumber of tests: 10\n\tAccuracy: 70%\nEND time: 16:12:03 8/23/2016\n\n************ Training k = 10 ************\nSTART time: 16:12:03 8/23/2016\nTesting k = 10\n\tNumber of training instances: 90\n\tNumber of tests: 10\n\tAccuracy: 70%\nEND time: 16:12:08 8/23/2016\n\nTRAINING ENDED!\n\twith 10-fold cross validation.\n\tEND time: 16:12:08 8/23/2016\n\n\tTotal num of tests: 100\n\tMean accuracy: 70%\n\t95% Confidence Interval: 64.4563% to 75.5437%\t[70% ± 5.54372%]\n\t***********************************************************\n\t* Best performing NN is net No. 2 with accuracy of: 80%\n\t***********************************************************\n\n\n\nParsing TESTING data...\nNumber of instances: 10\nSize of inputs: 10 and size of targetOutputs: 10\nSize of inputs matrices: 1,784 and size of targetOutputs matrices: 1,10\n\n\nTESTING BEGINS!\n\tNumber of successful classifications: 8\n\tNumber of failed classifications: 2\n\tAccuracy: 0.8\nTESTING ENDED!\n" }, { "path": "saved_nets/2016-8-23--16-12-08.nn", "content": "784 397 10 1 0.1\n397 784\n0.0867619 -0.0636453 -0.000608821 -0.00833045 -0.00219213 0.0261956 0.0307975 -0.0017341 -0.0738728 0.0147178 -0.0203533 0.00453312 0.0500022 0.0458628 -0.0532476 0.0168789 0.00195695 -0.0106667 0.0143275 0.0404374 -0.0634341 0.0481834 -0.00449774 -0.0475071 -0.000424981 0.00781941 0.0164876 -0.0805206 -0.0742661 -0.020743 0.00027235 -0.0506898 -0.0466375 0.000960289 0.0576524 0.0497913 -0.0470938 -0.0142773 0.0443655 0.0164223 -0.0870222 -0.0178052 -0.0983863 -0.0279419 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-0.0939236 -0.189636 -0.200176 -1.0656 -0.646611 0.062632 -0.100633 -0.151071 -0.227468 -0.257408 0.320953 -0.174191 -0.534426 -0.249625 0.0233331 -0.228557 -0.188735 -0.652275 -0.419662 -0.0141376 -0.200372 -0.285681 0.129062 -0.429811 0.281015 -0.323514 -0.0134186 -0.104516 -0.281426 -0.0827232 -0.233407 0.000765397 -0.0383034 -0.964257 -0.147326 -0.317225 0.030558 -0.630571 0.0853966 -0.334603 -0.419101 -0.674391 -0.189153 -0.184544 0.281501 -0.0881777 0.165657 -0.491218 -0.431135 0.0118412 -0.257195 -0.446649 -0.195025 -0.863397 -0.0937466 -0.799268 -0.33638 0.41951 0.0874427 -0.259695 -0.807504 -0.301695 -0.266309 -0.594339 -0.145455 -0.119064 -0.274217 0.134407 -0.278718 0.467017 -0.399498 0.499794 -0.204846 -1.34747 -0.729226 0.314799 -0.0127978 0.216093 -0.507611 -0.449608 -0.581655 0.0949996 -0.252415 0.149586 -1.06127 0.892899 0.107175 -0.408457 -0.15566 -0.116356 -0.223235 0.3286 -0.264515 -0.383312 0.308246 -0.066031 0.0146313 -0.589038 0.377977 -0.496012 0.0479091 -0.17609 -0.945241 -1.11019 -0.215626 -0.184382 0.149376 -0.334941 0.19753 0.208594 -0.401566 0.0720668 -0.270948 -0.621153 -0.838612 0.46567 -0.824513 -0.277786 -0.321706 0.168127 -0.0369216 -0.136412 -0.620607 -0.38988 -0.885184 -0.653763 0.701705 0.300228 -0.680092 -0.0814558 0.385558 -0.676867 -0.754753 0.0611888 0.0443867 -0.412672 0.165452 0.111273 0.517488 -0.623236 0.262991 0.17014 -0.125464 0.172573 0.208038 -0.754974 -0.685899 0.0108367 0.0302802 -0.11596 -0.399382 0.0271354 -0.249468 -0.348124 0.11353 -0.283833 0.0519984 0.0841891 0.414193 -0.481273 -0.0494506 -0.319008 0.206942 -0.136411 -0.0782786 -0.200616 0.186903 -0.0554635 -0.266115 -0.491041 -0.454958 0.117457 -0.214705 -0.559765 -0.633171 -0.0170519 -0.34126 -0.151345 -0.117032 -0.0819686 0.0998235 -0.588928 0.168367 0.310683 -0.212046 -0.538723 -0.373166 0.298849 0.139124 -1.21853 -0.0483019 0.089209 -0.40283 -0.361599 0.493815 -0.0866676 0.126634 -0.152336 -1.42335 -0.197006 -0.270202 0.367645 -0.601305 -0.257936 0.19487 0.541322 0.599732 -0.641114 -0.203853 0.191443 0.124479 -0.414149 0.192402 0.0590139 -0.163079 0.0289392 -0.344946 -0.30337 0.0281904 -0.564272 0.331737 -0.288739 -0.632125 -0.153252 -0.284804 0.151187 -0.0884237 -0.507377 -0.156635 0.435795 -0.311593 \n-0.193196 -0.295086 -0.500388 -0.0329784 -0.403553 0.0707392 0.361165 -0.40406 -0.239176 0.382643 0.386641 -0.557429 -0.146041 -0.202014 0.146357 -0.01344 -0.00623582 -0.269308 -1.07621 0.135019 -0.690325 -0.351674 0.128087 -0.28881 0.200632 0.0287878 0.61257 -0.270645 -0.0684828 -0.684205 -0.773764 -0.858727 -1.39894 -0.813244 -0.197106 -0.0581443 -0.129798 -1.29442 0.466083 -0.0509996 -1.29658 -0.0796559 -0.0851958 0.154015 -0.0981881 -0.181752 -0.315348 -0.45418 -0.10371 0.350052 -0.0610217 -0.4099 -0.273445 -0.191771 -0.144554 0.302323 0.039893 0.18437 -0.072512 -1.01635 0.0124174 -0.867175 -0.0963984 -0.200277 -0.205282 -0.277899 -0.462438 -0.438689 -0.147149 0.514884 -0.0209913 -0.165711 0.219544 -0.464097 0.180722 -0.350985 -0.706259 -0.691146 0.0626718 -0.00728678 -0.621299 -0.32807 0.0951437 -0.0295089 -0.0646108 -1.23174 0.108984 -0.112863 -1.284 -0.415443 -0.420989 -0.205867 0.070322 -0.0597718 -0.491667 -0.407939 0.352779 -0.244989 -0.734863 -0.221819 -0.216401 -0.543574 0.129193 -0.0918468 0.0686659 0.0848069 -0.32864 -0.914478 -0.565118 0.163741 -0.230766 -0.0538264 0.467462 0.544783 -0.617855 -0.162975 -0.238043 -0.452888 -0.189228 0.161509 -0.480072 0.164421 -0.423743 0.385942 -0.224514 -0.152208 0.143848 -0.422638 0.115359 -0.250023 -0.493363 -0.41502 -0.145695 0.4357 -0.396375 -0.437552 -0.511419 -0.125031 0.181243 -0.550708 0.0197371 -0.29197 0.356056 -0.0911994 -0.0701998 0.495542 -0.26278 0.0154844 0.591281 -1.04372 1.16291 -0.305563 -0.470592 -0.284229 -1.06194 -0.226527 -0.376142 0.125051 -0.129232 0.431795 -0.237101 -0.173731 -0.479645 -0.403355 -0.442868 0.506186 -0.118094 -0.0390582 -0.579481 -0.224921 -0.448178 -0.0969348 -0.243304 -0.125627 -0.322393 0.288035 -0.0769623 0.148122 -0.503147 0.430744 0.107984 0.130254 -0.0206959 -0.329931 -0.0739203 0.152079 -0.0208118 1.03267 0.307748 0.108472 0.411832 -0.222063 -0.211136 -0.115394 0.312364 0.192049 -0.296719 0.118063 -0.14027 -0.873735 -0.400597 -0.196928 0.023977 0.160349 -0.0928103 -0.389668 0.243148 0.206708 0.141174 -0.742772 0.465486 -0.205089 0.032424 0.173999 0.114202 -0.99742 0.652211 -0.20115 0.115862 -0.22951 -0.124739 0.155021 0.457956 0.295325 -0.245474 -0.0914523 -0.196055 -0.712069 -0.0885229 0.474549 0.0590407 -0.350609 0.494626 0.211277 0.179009 0.439611 -0.534055 -0.391844 0.448314 0.0458368 -0.439655 0.667454 -0.525789 -0.325406 -0.348172 0.106947 0.300109 -0.963585 0.0911205 -0.0554433 -0.155988 -0.287639 -0.3571 0.29218 -0.754377 0.0121822 -0.503614 -0.336904 0.630979 0.550194 0.0343199 -0.635768 -0.200891 -0.114164 -0.497277 -0.921716 0.273662 -0.0925946 -0.853003 0.330196 0.066586 -0.368999 0.069316 -0.174651 -0.557139 -0.289018 0.457497 0.340566 0.125016 -0.166562 -0.253727 -0.987636 0.0183921 -0.182351 -0.111407 -0.216765 -0.539693 -0.5808 0.984793 -0.471205 -0.200693 0.12644 -0.505871 0.203717 -0.219145 -0.21304 -0.291986 -0.554401 0.13091 -1.18495 -0.0345443 0.155967 -0.0716856 0.731997 0.183442 0.625851 -0.344687 -0.167219 0.438137 -0.244724 -0.298646 0.111293 0.0257104 -0.0487711 -0.252532 -0.34023 -0.0607595 -0.0426034 -0.457852 0.0501289 0.196815 -0.578467 -0.045007 -0.77338 0.0276062 -0.61037 -0.175077 0.290028 0.310915 -0.797134 -0.166531 0.0576573 -0.523145 -0.591493 -0.398057 -0.294464 -0.471949 -0.902095 -0.0567689 -0.375192 -0.136401 0.202186 -0.475826 -0.534012 -0.380345 -0.68584 -0.142624 -0.0045899 -0.849122 -0.10699 0.051978 -0.224463 -0.546182 0.022386 -0.458408 -0.778048 -0.102727 -0.483417 0.0822012 0.0325634 0.580009 -0.762251 -0.00928082 0.140748 -0.547359 -0.00559185 0.476881 -0.127459 -0.197015 -0.45121 -0.17074 -0.484469 -0.221491 -0.214439 -0.243187 0.0618228 0.0257461 0.0149321 -0.211742 0.000975637 -0.535268 -0.626607 -0.122867 -0.26992 -0.180582 0.168981 0.00834005 -0.0460507 -0.589666 -0.162142 0.13083 -0.193564 -0.364362 \n" }, { "path": "tests.cpp", "content": "//\n// tests.cpp\n// Neural Net\n//\n// A bunch of tests for Matrix.hpp & NeuralNet.hpp\n//\n// Created by Gil Dekel on 8/19/16.\n// Last edited by Gil Dekel on 9/4/16.\n//\n\n#include \n#include \n#include //std::rand, std::srand\n#include //std::accumulate\n#include \n#include \n#include \n#include \n\n#include \"Matrix.hpp\"\n#include \"NeuralNet.hpp\"\n\n\nint myrandom(int i) { return std::rand()%i;}\n\nvoid parseInput(const std::string &fileName, std::vector &inputs, std::vector &targetOutputs) {\n std::ifstream in(fileName);\n if (in.fail()) {\n std::cout << \"ERROR:: CANNOT READ FROM FILE: '\" << fileName << \"'\\n\";\n exit(1);\n }\n\n std::string instance;\n while (getline(in, instance)) {\n std::stringstream ss(instance);\n size_t target;\n double pixel;\n\n ss >> target;\n Matrix newTarget(1,10);\n for (int m = 0; m < newTarget.getNumOfRows(); ++m) {\n for (int n = 0; n < newTarget.getNumOfCols(); ++n) {\n newTarget(m,n) = 0.01;\n }\n }\n newTarget(0,target) = 0.99;\n\n targetOutputs.push_back(std::move(newTarget));\n// std::cout << \"Max value is at: \" << result.first << \",\" << result.second << \" and the value is: \" << result.second << std::endl << std::endl << std::endl;\n\n Matrix instanceInput(1,784);\n size_t count = 0;\n while (ss >> pixel) {\n instanceInput(0,count) = ((pixel/255)*0.99)+0.01;\n ++count;\n }\n inputs.push_back(std::move(instanceInput));\n }\n \n}\n\nstd::string getCurrTime() {\n time_t t = time(0); // get time now\n struct tm * now = localtime( & t );\n std::string currTime = ((now->tm_hour < 10) ? \"0\" + std::to_string(now->tm_hour) : std::to_string(now->tm_hour));\n currTime += \":\" + ((now->tm_min < 10) ? \"0\" + std::to_string(now->tm_min) : std::to_string(now->tm_min));\n currTime += \":\" + ((now->tm_sec < 10) ? \"0\" + std::to_string(now->tm_sec) : std::to_string(now->tm_sec));\n currTime += ' ' + std::to_string(now->tm_mon + 1) + '/' + std::to_string(now->tm_mday) + '/' + std::to_string(now->tm_year + 1900);\n\n return currTime;\n}\n\nint main(int argc, const char * argv[]) {\n// ****************************************************************************************************************\n// Matrix Class Tests\n// ****************************************************************************************************************\n// \n// size_t m = 1000;\n// size_t n = 1000;\n//\n// Matrix mtrx(m,n);\n// Matrix mtrxB(n,m);\n//\n// int count = 1;\n// for (int i = 0; i < mtrxB.getNumOfRows(); ++i) {\n// for (int j = 0; j < mtrxB.getNumOfCols(); ++j) {\n// mtrx(j,i) = 2;\n// mtrxB(i,j) = count;\n// ++count;\n// }\n// }\n//\n// std::cout << \"Initialize mrtx\\n\";\n// mtrx.printMtrx();\n\n// std::cout << \"Initialize mrtxB\\n\";\n// mtrxB.printMtrx();\n\n// std::cout << \"Transpose mrtxB\\n\";\n// Matrix B_T = mtrxB.T();\n// B_T.printMtrx();\n \n//\n// std::cout << \"mtrx+B_T:\\n\";\n// (mtrx+B_T).printMtrx();\n//\n// std::cout << \"B_T+mtrx:\\n\";\n// (B_T+mtrx).printMtrx();\n// \n// std::cout << \"mtrx += 10\\n\";\n// (mtrx += 10).printMtrx();\n// \n// std::cout << \"mtrx += B_T:\\n\";\n// (mtrx += B_T).printMtrx();\n// \n// std::cout << \"mtrx:\\n\";\n// mtrx.printMtrx();\n//\n// std::cout << \"mtrx-B_T:\\n\";\n// (mtrx-B_T).printMtrx();\n// \n// std::cout << \"B_T-mtrx:\\n\";\n// (B_T-mtrx).printMtrx();\n// \n// std::cout << \"mtrx -= 10\\n\";\n// (mtrx -= 10).printMtrx();\n// \n// std::cout << \"mtrx -= B_T:\\n\";\n// (mtrx -= B_T).printMtrx();\n// \n// std::cout << \"mtrx:\\n\";\n// mtrx.printMtrx();\n//\n// std::cout << \"mtrx-B_T:\\n\";\n// (mtrx-B_T).printMtrx();\n//\n// std::cout << \"B_T-mtrx:\\n\";\n// (B_T-mtrx).printMtrx();\n//\n// std::cout << \"mtrx*B_T:\\n\";\n// (mtrx*B_T).printMtrx();\n//\n// std::cout << \"B_T*mtrx:\\n\";\n// (B_T*mtrx).printMtrx();\n//\n// std::cout << \"mtrx+2:\\n\";\n// (mtrx+2).printMtrx();\n//\n// std::cout << \"2+mtrx:\\n\";\n// (2+mtrx).printMtrx();\n//\n// std::cout << \"mtrx-2:\\n\";\n// (mtrx-2).printMtrx();\n//\n// std::cout << \"2-mtrx:\\n\";\n// (2-mtrx).printMtrx();\n//\n// std::cout << \"mtrx*2:\\n\";\n// (mtrx*2).printMtrx();\n// \n// std::cout << \"2*mtrx:\\n\";\n// (2*mtrx).printMtrx();\n// \n// std::cout << \"mtrx/B_T:\\n\";\n// (mtrx/B_T).printMtrx();\n// \n// std::cout << \"B_T/mtrx:\\n\";\n// (B_T/mtrx).printMtrx();\n// \n// std::cout << \"mtrx/2:\\n\";\n// (mtrx/2).printMtrx();\n// \n// std::cout << \"2/mtrx:\\n\";\n// (2/mtrx).printMtrx();\n// \n// std::cout << \"mtrx/=2:\\n\";\n// (mtrx/=2).printMtrx();\n//\n// std::cout << \"mtrx*=2:\\n\";\n// (mtrx*=2).printMtrx();\n// \n// std::cout << \"mtrx*=2:\\n\";\n// (mtrx*=2).printMtrx();\n//\n// \n// std::cout << \"neg = -mtrx:\\n\";\n// Matrix neg{-mtrx};\n// neg.printMtrx();\n// \n// std::cout << \"-neg:\\n\";\n// (-neg).printMtrx();\n//\n// std::cout << \"mtrx.dot(mtrxB):\\n\";\n// auto t_start = std::chrono::high_resolution_clock::now();\n// mtrx.dot(mtrxB);\n//// (mtrx.dot(mtrxB)).printMtrx();\n// auto t_end = std::chrono::high_resolution_clock::now();\n// auto end = std::chrono::duration(t_end-t_start).count();\n// std::cout << \"END time: \" << end/1000 << \"s\" << std::endl;\n//\n// std::cout << \"mtrxB.dot(mtrx):\\n\";\n// t_start = std::chrono::high_resolution_clock::now();\n// mtrxB.dot(mtrx);\n//// (mtrxB.dot(mtrx)).printMtrx();\n// t_end = std::chrono::high_resolution_clock::now();\n// end = std::chrono::duration(t_end-t_start).count();\n// std::cout << \"END time: \" << end/1000 << \"s\" << std::endl;\n//\n// throw MatrixDimensionsMismatch();\n// throw MatrixInnderDimensionsMismatch();\n \n// ****************************************************************************************************************\n// NeuralNet Class Tests\n// ****************************************************************************************************************\n NeuralNet NN(784, 397, 10, 1, 0.1);\n \n std::cout << \"Parsing TESTING data...\\n\";\n \n std::vector testInputs;\n std::vector testTargetOutputs;\n parseInput(\"data/training_data/mnist_train_100.txt\", testInputs, testTargetOutputs);\n std::cout << \"Number of instances: \" << testInputs.size() << std::endl;\n std::cout << \"Size of inputs: \" << testInputs.size() << \" and size of targetOutputs: \" << testTargetOutputs.size() << std::endl;\n std::cout << \"Size of inputs matrices: \" << testInputs[0].getNumOfRows() << \",\" << testInputs[0].getNumOfCols() << \" and size of targetOutputs matrices: \" << testTargetOutputs[0].getNumOfRows() << \",\" << testTargetOutputs[0].getNumOfCols() << std::endl;\n \n \n double numOfTests = testInputs.size();\n double success = 0;\n double fails = 0;\n \n std::cout << \"\\n\\nTESTING BEGINS!\" << std::endl;\n auto t_start = std::chrono::high_resolution_clock::now();\n for (int i = 0; i < testInputs.size(); ++i) {\n Matrix result = NN.queryNet(testInputs[i]);\n \n std::pair resultVal = result.getMaxVal();\n std::pair targetVal = testTargetOutputs[i].T().getMaxVal();\n \n if (resultVal == targetVal) {\n ++success;\n } else {\n ++fails;\n }\n \n }\n auto t_end = std::chrono::high_resolution_clock::now();\n auto end = std::chrono::duration(t_end-t_start).count();\n \n std::cout << \"\\tNumber of successful classifications: \" << success << \"\\n\\tNumber of failed classifications: \" <<\n fails << \"\\n\\tAccuracy: \" << success/numOfTests << std::endl;\n std::cout << \"TESTING ENDED!\\nEND time: \" << end/1000 << \"s\" << std::endl;\n\n NN.loadNetwork(\"saved_nets/2016-8-24--07-12-33.nn\");\n \n std::cout << \"Parsing TESTING data...\\n\";\n \n numOfTests = testInputs.size();\n success = 0;\n fails = 0;\n \n std::cout << \"\\n\\nTESTING BEGINS!\" << std::endl;\n t_start = std::chrono::high_resolution_clock::now();\n for (int i = 0; i < testInputs.size(); ++i) {\n Matrix result = NN.queryNet(testInputs[i]);\n \n std::pair resultVal = result.getMaxVal();\n std::pair targetVal = testTargetOutputs[i].T().getMaxVal();\n \n if (resultVal == targetVal) {\n ++success;\n } else {\n ++fails;\n }\n \n }\n t_end = std::chrono::high_resolution_clock::now();\n end = std::chrono::duration(t_end-t_start).count();\n \n std::cout << \"\\tNumber of successful classifications: \" << success << \"\\n\\tNumber of failed classifications: \" <<\n fails << \"\\n\\tAccuracy: \" << success/numOfTests << std::endl;\n std::cout << \"TESTING ENDED!\\nEND time: \" << end/1000 << \"s\" << std::endl;\n \n return 0;\n}\n" } ]